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Page history last edited by mike@mbowles.com 10 years ago

 

Machine Learning 201

 

Instructor: Dr. Michael Bowles & Dr. Patricia Hoffman

 

Overview of the Course

Machine Learning 201 and 202 cover topics in greater depth than 101 and 102.  Participants in the class should come away able to read the current literature and apply what they read to their own work.  Machine Learning 201 and 202 can be taken in any order. 

 

Machine Learning 201 begins with ordinary least squares regression and extends this basic tool in a number of directions.  We'll consider various regularization approaches.  We'll introduce logistic regression and we'll learn how to code categorical inputs and outputs. We'll look at feature space expansions.  These will lead naturally to generalizations of linear regression, known as  the "generalized linear model" and the "generalized additive model". 

 

Text:  "The Elements of Statistical Learning - Data Mining, Inference, and Prediction"  by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

 

See also Prof Robert Tibshirani's notes for stats 315a: http://www-stat.stanford.edu/~tibs/stat315a.html

 

Prerequisites

Machine Learning 201 and 202 employ beginner-level probability, calculus and linear algebra (e.g. preruse the appendices in "Introduction to Data Mining" by Tan et. al. or Linear Algebra, and Probability Theory.)  If you have taken Machine Learning 101 and 102 classes, you are well prepared for this course, but those are not required to start 201.

 

Participants should be familiar with R or be willing to pick R up outside of class.  We will hand out R-code for most of our examples, but we won't spend time in 201 going through introductory material on R.  Come to the first class with R loaded on your computer.  http://cran.r-project.org/  For your review, R are here: References for R,  Reference for R Comments,  More R references.  To integrate R with Eclipse click here

 

To get the most out of the class, participants will need to work through the homework assignments. 

 

General Sequence of Classes:

Machine Learning 101:   Supervised learning

Text: "Introduction to Data Mining", by Pang-Ning Tan, Michael Steinbach and Vipin Kumar

Machine Learning 102Unsupervised Learning and Fault Detection

Text: "Introduction to Data Mining", by Pang-Ning Tan, Michael Steinbach and Vipin Kumar

 

Machine Learning 201:    Advanced Regression Techniques, Generalized Linear Models, and Generalized Additive Models    

Text:  "The Elements of Statistical Learning - Data Mining, Inference, and Prediction"  by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

 

Machine Learning 202:   Collaborative Filtering, Bayesian Belief Networks, and Advanced Trees

Text:  "The Elements of Statistical Learning - Data Mining, Inference, and Prediction"  by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

 

Machine Learning Big Data:  Adaptation and execution of machine learning algorithms in the map reduce framework

 

Machine Learning Text Processing:  Machine learning applied to natural language text documents using statistical algorithms including  indexing, automatic classification (e.g. spam filtering) part of speech identification, topic and modeling, sentiment extraction

 

Future Topics 

     Data Mining Social Networks

     Text Mining

     Recommender Methods

     Big Data

 

Machine Learning 201 Syllabus:  

 

Week  Topics  Homework  Links 
       
1st Week  Advanced Regression Topics
 

Lecture 1 and 2

 

      6/1/2011 Ordinary Least Squares - error bounds    
  Subset Select, fwd & backward step-wise    
  Least Angle Regression - LARS    
  Attribute basis change    
      6/2/2011 Coefficient shrinkage methods
Homework01.pdf    
  L1, L2 coefficient penalties
   
  Ridge, lasso and elastic net    
       
       
2nd Week      Regression Topics    Lecture 3 and 4  
    6/8/2011 Logistic Regression
HW #1 Due   
    6/9/2011
Attribute Expansion
Homework02.pdf    
       
   
   
3rd Week  Factor Inputs/Outputs 
 
NotesWeek3  
   6/15/2011
Coding for Factor Inputs
HW #2 Due  
   6/16/2011
Error-correcting codes
 Homework03.pdf
 
       
4th Week  Generalized Linear Models
  NotesWeek4
    6/22/2011
   HW #3 Due   
    6/23/2011
  Homework04.pdf    
       
5th Week  Paper on glmnet
http://www.jstatsoft.org/v33/i01/paper   NotesWeek5  
   6/29/2011
 
HW #4 Due BonusTopics  
   6/30/2011
  Homework5    
       

 

We will be using the following text as a reference for the 201 and 202

 

"The Elements of Statistical Learning - Data Mining, Inference, and Prediction"  by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.  This is an excellent book.  Virtually everyone in the field knows it and uses it as a standard reference.  This book is free to look at on line.  http://www-stat.stanford.edu/~tibs/ElemStatLearn

 

Anyone can read this web site, however only the instructors have permission to edit the site. 

 

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